Grouping Gene Ontology terms to improve the assessment of gene set enrichment in microarray data

Background  

Gene Ontology (GO) terms are often used to assess the results of microarray experiments. The most common way to do this is to perform Fisher's exact tests to find GO terms which are over-represented amongst the genes declared to be differentially expressed in the analysis of the microarray experiment. However, due to the high degree of dependence between GO terms, statistical testing is conservative, and interpretation is difficult.     

Circular RNA expression profiles during the differentiation of mouse neural stem cells

Background

Circular RNAs (circRNAs) have recently been found to be expressed in human brain tissue, and many lines ofevidence indicate that circRNAs play regulatory roles in neurodevelopment. Proliferation and differentiation of neural stem cells (NSCs) are critical parts during development of central nervous system (CNS).To date, there have been no reports ofcircRNA expression profiles during the differentiation of mouse NSCs. We hypothesizethat circRNAs mayregulate gene expression in the proliferation anddifferentiation of NSCs.

Results

In this study, we obtained NSCs from the wild-type C57BL/6 J mouse fetal cerebral cortex. We extracted total RNA from NSCs in different differentiation stagesand then performed RNA-seq. By analyzing the RNA-Seq data, we found 37circRNAs and 4182 mRNAs differentially expressedduringthe NSC differentiation. Gene Ontology (GO) enrichment analysis of thecognate linear genes of these circRNAsrevealed that some enriched GO terms were related to neural activity. Furthermore, we performed a co-expression network analysis of these differentially expressed circRNAs and mRNAs. The result suggested a stronger GO enrichmentin neural features for both the cognate linear genes of circRNAs and differentially expressed mRNAs.

Conclusion

We performed the first circRNA investigation during the differentiation of mouse NSCs. Wefound that12 circRNAs might have regulatory roles duringthe NSC differentiation, indicating that circRNAs might be modulated during NSC differentiation.Our network analysis suggested the possible complex circRNA-mRNA mechanisms during differentiation, and future experimental workis need to validate these possible mechanisms.

     

Gene Expression Profiling Unravels Cancer-Related Hepatic Molecular Signatures in Steatohepatitis but Not in Steatosis

Background

Pathogenesis and factors for determining progression of alcoholic and non-alcoholic steatosis to steatohepatitis with risk of further progression to liver cirrhosis and cancer are poorly understood. In the present study, we aimed to identify potential molecular signatures for discrimination of steatohepatitis from steatosis.

Methodology and Results

Global microarray gene expression analysis was applied to unravel differentially expressed genes between steatohepatitis compared to steatosis and control samples. For functional annotation as well as the identification of disease-relevant biological processes of the differentially expressed genes the gene ontology (GO) database was used. Selected candidate genes (n = 46) were validated in 87 human liver samples from two sample cohorts by quantitative real-time PCR (qRT-PCR). The GO analysis revealed that genes down-regulated in steatohepatitis were mainly involved in metabolic processes. Genes up-regulated in steatohepatitis samples were associated with cancer progression and proliferation. In surgical liver resection samples, 39 genes and in percutaneous liver biopsies, 30 genes were significantly up-regulated in steatohepatitis. Furthermore, immunohistochemical investigation of human liver tissue revealed a significant increase of AKR1B10 protein expression in steatohepatitis.

Conclusions

The development of steatohepatitis is characterized by distinct molecular changes. The most striking examples in this respect were KRT23 and AKR1B10, which we found to be highly differentially expressed in steatohepatitis compared to steatosis and normal liver. We propose that KRT23 and AKR1B10 may serve as future potential biomarkers for steatohepatitis as well as markers for progression to HCC.     

Principal components analysis based methodology to identify differentially expressed genes in time-course microarray data

Background  

Time-course microarray experiments are being increasingly used to characterize dynamic biological processes. In these experiments, the goal is to identify genes differentially expressed in time-course data, measured between different biological conditions. These differentially expressed genes can reveal the changes in biological process due to the change in condition which is essential to understand differences in dynamics.     

Dissecting systems-wide data using mixture models: application to identify affected cellular processes

Background  

Functional analysis of data from genome-scale experiments, such as microarrays, requires an extensive selection of differentially expressed genes. Under many conditions, the proportion of differentially expressed genes is considerable, making the selection criteria a balance between the inclusion of false positives and the exclusion of false negatives.     

Gene selection with multiple ordering criteria

Background  

A microarray study may select different differentially expressed gene sets because of different selection criteria. For example, the fold-change and p-value are two commonly known criteria to select differentially expressed genes under two experimental conditions. These two selection criteria often result in incompatible selected gene sets. Also, in a two-factor, say, treatment by time experiment, the investigator may be interested in one gene list that responds to both treatment and time effects.     

A comparison of probe-level and probeset models for small-sample gene expression data

Background  

Statistical methods to tentatively identify differentially expressed genes in microarray studies typically assume larger sample sizes than are practical or even possible in some settings.     

CoXpress: differential co-expression in gene expression data

Background  

Traditional methods of analysing gene expression data often include a statistical test to find differentially expressed genes, or use of a clustering algorithm to find groups of genes that behave similarly across a dataset. However, these methods may miss groups of genes which form differential co-expression patterns under different subsets of experimental conditions. Here we describe coXpress, an R package that allows researchers to identify groups of genes that are differentially co-expressed.     

A mRNA landscape of bovine embryos after standard and MAPK-inhibited culture conditions: a comparative analysis

Background

Genes and signalling pathways involved in pluripotency have been studied extensively in mouse and human pre-implantation embryos and embryonic stem (ES) cells. The unsuccessful attempts to generate ES cell lines from other species including cattle suggests that other genes and pathways are involved in maintaining pluripotency in these species. To investigate which genes are involved in bovine pluripotency, expression profiles were generated from morula, blastocyst, trophectoderm and inner cell mass (ICM) samples using microarray analysis. As MAPK inhibition can increase the NANOG/GATA6 ratio in the inner cell mass, additionally blastocysts were cultured in the presence of a MAPK inhibitor and changes in gene expression in the inner cell mass were analysed.

Results

Between morula and blastocyst 3,774 genes were differentially expressed and the largest differences were found in blastocyst up-regulated genes. Gene ontology (GO) analysis shows lipid metabolic process as the term most enriched with genes expressed at higher levels in blastocysts. Genes with higher expression levels in morulae were enriched in the RNA processing GO term. Of the 497 differentially expressed genes comparing ICM and TE, the expression of NANOG, SOX2 and POU5F1 was increased in the ICM confirming their evolutionary preserved role in pluripotency. Several genes implicated to be involved in differentiation or fate determination were also expressed at higher levels in the ICM. Genes expressed at higher levels in the ICM were enriched in the RNA splicing and regulation of gene expression GO term. Although NANOG expression was elevated upon MAPK inhibition, SOX2 and POU5F1 expression showed little increase. Expression of other genes in the MAPK pathway including DUSP4 and SPRY4, or influenced by MAPK inhibition such as IFNT, was down-regulated.

Conclusion

The data obtained from the microarray studies provide further insight in gene expression during bovine embryonic development. They show an expression profile in pluripotent cells that indicates a pluripotent, epiblast-like state. The inability to culture ICM cells as stem cells in the presence of an inhibitor of MAPK activity together with the reported data indicates that MAPK inhibition alone is not sufficient to maintain a pluripotent character in bovine cells.

Electronic supplementary material

The online version of this article (doi:10.1186/s12864-015-1448-x) contains supplementary material, which is available to authorized users.     

Identification of genes differentially expressed during larval molting and metamorphosis of <Emphasis Type="Italic">Helicoverpa armigera</Emphasis>

Background  

Larval molting and metamorphosis are important physiological processes in the life cycle of the holometabolous insect. We used suppression subtractive hybridization (SSH) to identify genes differentially expressed during larval molting and metamorphosis.     

CoPub Mapper: mining MEDLINE based on search term co-publication

Background  

High throughput microarray analyses result in many differentially expressed genes that are potentially responsible for the biological process of interest. In order to identify biological similarities between genes, publications from MEDLINE were identified in which pairs of gene names and combinations of gene name with specific keywords were co-mentioned.     

Density based pruning for identification of differentially expressed genes from microarray data

Motivation

Identification of differentially expressed genes from microarray datasets is one of the most important analyses for microarray data mining. Popular algorithms such as statistical t-test rank genes based on a single statistics. The false positive rate of these methods can be improved by considering other features of differentially expressed genes.

Results

We proposed a pattern recognition strategy for identifying differentially expressed genes. Genes are mapped to a two dimension feature space composed of average difference of gene expression and average expression levels. A density based pruning algorithm (DB Pruning) is developed to screen out potential differentially expressed genes usually located in the sparse boundary region. Biases of popular algorithms for identifying differentially expressed genes are visually characterized. Experiments on 17 datasets from Gene Omnibus Database (GEO) with experimentally verified differentially expressed genes showed that DB pruning can significantly improve the prediction accuracy of popular identification algorithms such as t-test, rank product, and fold change.

Conclusions

Density based pruning of non-differentially expressed genes is an effective method for enhancing statistical testing based algorithms for identifying differentially expressed genes. It improves t-test, rank product, and fold change by 11% to 50% in the numbers of identified true differentially expressed genes. The source code of DB pruning is freely available on our website http://mleg.cse.sc.edu/degprune

     

Normal uniform mixture differential gene expression detection for cDNA microarrays

Background  

One of the primary tasks in analysing gene expression data is finding genes that are differentially expressed in different samples. Multiple testing issues due to the thousands of tests run make some of the more popular methods for doing this problematic.